NREL Unveils Groundbreaking Generative Machine Learning Model To Simulate Future Energy-Climate Impacts

New Open-Source, Publicly Available Model Rapidly Produces Super-Detailed Future Climate Data To Support Integrated Energy System Planning

“Sup3rCC will change the way we study and plan future energy systems,” said Dan Bilello, director of the Strategic Energy Analysis Center at NREL. “The tool produces foundational climate data that can be plugged into energy system models and provide much-needed insights for decision makers who are responsible for keeping the lights on.”Energy system research and climate research have traditionally been siloed for several reasons.

Climate and energy are increasingly intertwined. Now there is a tool to study them both. Photo by Billy Roberts, NREL “Generative machine learning is the cornerstone technology at the heart of our super-resolution approach,” said Ryan King, computational researcher at NREL and co-developer of Sup3rCC. “It would be impossible for us to produce these analyses without machine learning.”. The model then injects physically realistic small-scale information that it has learned from the data sets into the coarse future outputs from global climate models.

“Our approach dramatically reduces the computational cost of generating high spatial and temporal resolution data by several orders of magnitude,” King said. “This allows us to consider changes in renewable resources and electrical demand in a multitude of future climate scenarios across multiple decades, which is critical for planning future energy systems.

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